Problèmes de tournées avec gestion de stock et prise en compte explicite de la consommation d'énergie

Dans le problème de tournées avec gestion de stock ou "Inventory Routing Problem" (IRP), le fournisseur a pour mission de surveiller les niveaux de stock d'un ensemble de clients et gérer leur approvisionnement en prenant simultanément en compte les coûts de transport et de stockage. Etant données les nouvelles exigences de développement durable et de transport écologique, nous étudions l'IRP sous une perspective énergétique, peu de travaux s'étant intéressés à cet aspect. Plus précisément, la thèse identifie les facteurs principaux influençant la consommation d'énergie et évalue les gains potentiels qu'une meilleure planification des approvisionnements permet de réaliser. Un problème relatif à l'approvisionnement en composants de chaînes d'assemblage d'automobiles est tout d'abord considéré pour lequel la masse transportée, la dynamique du véhicule et la distance parcourue sont identifiés comme les principaux facteurs impactant la consommation énergétique. Ce résultat est étendu à l'IRP classique et les gains potentiels en termes d'énergie sont analysés. Un problème industriel de tournées avec gestion de stock est ensuite étudié et résolu, notamment à l'aide d'une méthode de génération de colonnes. Ce problème met en évidence les limitations du modèle IRP classique, ce qui nous a amené à définir un modèle d'IRP plus réaliste. Finalement, une méthode de décomposition basée sur la relaxation lagrangienne est développée pour la résolution de ce problème dans le but de minimiser la consommation énergétique.The thesis studies the Inventory Routing Problem (IRP) with explicit energy consideration. Under the Vendor Managed Inventory (VMI) model, the IRP is an integration of the inventory management and routing, where both inventory storage and transportation costs are taken into account. Under the new sustainability paradigm, green transport and logistics has become an emerging area of study, but few research focus on the ecological aspect of the classical IRP. Since the classical IRP concentrates solely on the economic benefits, it is worth studying under the energy perspective. The thesis gives an estimation of the energetic gain that a better supplying plan can provide. More specifically, this thesis integrates the energy consumption into the decision of the inventory replenishment and routing. It starts with a part supplying problem in car assembly lines, where the transported mass, the vehicle dynamics and the travelled distance are identified as main energy influencing factors. This result is extended to the classical IRP with energy objective to show the potential energy reduction that can be achieved. Then, an industrial challenge of IRP is presented and solved using a column generation approach. This problem put the limitations of the classical IRP model in evidence, which brings us to define a more realistic IRP model on a multigraph. Finally, a Lagrangian relaxation method is presented for solving this new model with the aim of energy minimization

Models, methods and algorithms for supply chain planning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An outline of supply chains and differences in the problem types is given. The motivation for a generic framework is discussed and explored. A conceptual model is presented along with it application to real world situations; and from this a database model is developed. A MIP and CP implementations are presented; along with alternative formulation which can be use to solve the problems. A local search solution algorithm is presented and shown to have significant benefits. Problem instances are presented which are used to validate the generic models, including a large manufacture and distribution problem. This larger problem instance is not only used to explore the implementation of the models presented, but also to explore the practically of the use of alternative formulation and solving techniques within the generic framework and the effectiveness of such methods including the neighbourhood search solving method. A stochastic dimension to the generic framework is explored, and solution techniques for this extension are explored, demonstrating the use of solution analysis to allow problem simplification and better solutions to be found. Finally the local search algorithm is applied to the larger models that arise from inclusion of scenarios, and the methods is demonstrated to be powerful for finding solutions for these large model that were insoluble using the MIP on the same hardware

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

Development of Cislunar Space Logistics Networks for Satellite Constellation Support Using Event-Driven Generalized Multi-Commodity Network Flows

As space becomes an increasingly congested domain, the risk of damage to satellite constellations is increasing. In response, there is an increasing need for capabilities for unmanned repair, refueling, and reconstitution (R3) of those constellations. Cislunar orbits offer a promising storage and low-cost transfer solution for on-orbit service vehicles and replacement satellites to leverage those capabilities. This research makes use of mixed-integer linear programming-based logistics models to determine the situations in which a cislunar mission architecture would offer a cost-effective alternative to Earth-based R3. The network models presented in this research make use of the latest developments in Event-Driven Generalized Multi-Commodity Network Flows (ED-GMCNF), a new method of optimization that enables variable time steps between events. This research combines a new version of an ED-GMCNF with cislunar trajectory optimization to evaluate both the feasibility of cislunar orbits as well as the potential effects of lunar fuel production on R3 costs. This investigation finds, through an exhaustive numerical simulation campaign, that cislunar logistics networks provide cost-effective means of R3 regiments for Earth-orbiting and cislunar satellites when a lunar fuel supply is taken into consideration. The ED-GMCNF methodology also offers a promising foundation for future work in the mission planning field

Generalized multi-commodity network flows : case studies in space logistics and complex infrastructure systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 153-157).In transition to a new era of human space exploration, the question is what the next-generation space logistics paradigm should be. The past studies on space logistics have been mainly focused on a "vehicle" perspective such as propulsive feasibility, cargo capacity constraints, and manifesting strategies, with the arbitrarily predetermined logistics network. But how do we select an optimal logistics network? Especially if we can utilize in-situ resources on the Moon and Mars, it will add complexity to network selection problem. The objective of this thesis is to develop a comprehensive graph-theoretic modeling framework to quantitatively evaluate and optimize space exploration logistics from a "network" perspective. In an attempt to create such a modeling framework, we develop a novel network flow model referred to as the generalized multi-commodity network flow (GMCNF) model. On top of the classical network flow problems, the GMCNF model proposed in this thesis introduces three types of matrix multiplications (requirement, transformation, and concurrency), and also allows loop edges associated with nodes (graph loops) and multiple edges between the same end nodes (multigraph). With this modification, the model can handle multiple commodities that interact with each other in the form of requirement at nodes, transformation on edges, and concurrency within edges. A linear programming (LP) formulation and a mixed integer linear programming (MILP) formulation of the GMCNF model are described in preparation for the two case studies. For the MILP formulation, in addition to the flow, we introduce two more variables, capacity expansion and decision binary, and additional constraints including the big-M method. The first case study applies the GMCNF LP model to human exploration of Mars. First we solve the baseline problem with a demand that is equivalent to that of the NASA's Mars Design Reference Architecture (DRA) 5.0 scenario. It is found that the solution saves 67.5% from the Mars DRA 5.0 reference scenario in terms of the initial mass in low-Earth orbit (IMLEO) primarily because chemical (LOX/LH2) propulsion is used along with oxygen-rich ISRU. We also present one possible scenario with two "gateway" resource depots at GTO and DTO with orbital transfer vehicles (OTVs) running in the cislunar and Martian systems. Then we solve variant problems that have different settings to see the effect of each factor. Findings include: taking advantage of oxygen-rich ISRU, LOX/LH2 is preferred to nuclear thermal rocket (NTR), the aerobraking option as well as ISRU availability on the Moon make great contributions in reducing the total mass to be launched from Earth, and as the ISRU production rate decreases, ISRU in each location becomes worthless at a certain threshold and the network topology changes toward direct paths using NTR. The other case study applies the GMCNF MILP model to the complex infrastructure systems in Saudi Arabia, focusing on the couplings between water and energy. Considering the capacity of the online infrastructures as of 2010 as a basis, we solve the problems with the 2030 demand and the 2050 demand. The objective function is a weighted sum of the total cost and the total CO2 emission. The key findings include: the network tends to be less connected, more isolated when putting more emphasis on minimizing the CO2 emissions, and some of the resulting networks suggest the possibility of the long-distance pipeline network connecting the west coast and the east coast via the central region (trans-peninsula pipeline).by Takuto IshimatsuPh.D

Development of a techno-economic energy model for low carbon business parks

To mitigate climate destabilisation, global emissions of human-induced greenhouse gases urgently need to be reduced, to be nearly zeroed at the end of the century. Clear targets are set at European level for the reduction of greenhouse gas emissions and primary energy consumption and for the integration of renewable energy. Carbon dioxide emissions from fossil fuel combustion in the industry and energy sectors account for a major share of greenhouse gas emissions. Hence, a low carbon shift in industrial and business park energy systems is called for. Low carbon business parks minimise energy-related carbon dioxide emissions by enhanced energy efficiency, heat recovery in and between companies, maximal exploitation of local renewable energy production, and energy storage, combined in a collective energy system. Moreover, companies with complementary energy profiles are clustered to exploit energy synergies. The design of low carbon energy systems is facilitated using the holistic approach of techno-economic energy models. These models take into account the complex interactions between the components of an energy system and assist in determining an optimal trade-off between energetic, economic and environmental performances. In this work, existing energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of energy models are selected and described. Subsequently, a practical categorisation is proposed, existing of energy system evolution, optimisation, simulation, accounting and integration models, while key model features are compared. Next, essential features for modelling energy systems at business park scale are identified: As a first key feature, a superstructure-based optimisation approach avoids the need for a priori decisions on the system’s configuration, since a mathematical algorithm automatically identifies the optimal configuration in a superstructure that embeds all feasible configurations. Secondly, the representation of time needs to incorporate sufficient temporal detail to capture important characteristics and peaks in time-varying energy demands, energy prices and operation conditions of energy conversion technologies. Thirdly, energy technologies need to be accurately represented at equipment unit level by incorporating part-load operation and investment cost subject to economy of scale in the model formulation. In addition, the benefits of installing multiple units per technology must be considered. A generic model formulation of technology models facilitates the introduction of new technology types. As a fourth important feature, the potential of thermodynamically feasible heat exchange between thermal processes needs to be exploited, while optimally integrating energy technologies to fulfil remaining thermal demands. For this purpose, thermal streams need to be represented by heat –temperature profiles. Moreover, restrictions to direct heat exchange between process streams need to be taken into account. Finally, the possibility for energy storage needs to be included to enhance the integration of non-dispatchable renewable energy technologies and to bridge any asynchrony between cooling and heating demands. Starting from these essential features, a techno-economic optimisation model (Syn-E-Sys), is developed customised for the design of low carbon energy systems on business park scale. The model comprises two sequential stages. In the first stage, heat recovery within the system is maximised, while energy supply and energy storage technologies are optimally integrated and designed to fulfil remaining energy requirements at minimum total annualised costs. Predefined variations in thermal and electrical energy demand and supply are taken into account, next to a carbon emission cap. At the same time, heat networks can be deployed to transfer heat between separate parts of the system. In the second stage, the model generates an optimal multi-period heat exchanger network enabling all required heat exchanges. Syn-E-Sys builds upon a multi-period energy integration model that can deal with restrictions in heat exchange. It is combined with a generic technology model, that features part-load operation as well as investment cost subject to economy of scale, and a generic energy storage model. The technology model can be manipulated to represent various thermal or electrical energy conversion technology units, and serves as a building block to model more complex technologies. The storage model covers electrical as well as thermal energy storage, taking into account the effect of hourly energy losses on the storage level, without increasing the number of time steps to be analysed. For this purpose, time sequence is introduced by dividing the year into a set of time slices and assigning them to a hierarchical time structure. In addition, a more complex model for storage of sensible heat is integrated, consisting of a stack of interconnected virtual tanks. To enable the optimisation of the number of units per technology in the energy system configuration, an automated superstructure expansion procedure is incorporated. Heat transfer unit envelope curves are calculated to facilitate the choice of appropriate temperature levels for heat networks. Heat networks that are embedded within this envelope, completely avoid the increase in energy requirements that would result from the heat exchange restrictions between separated parts of the energy system. Finally, the heat exchanger network is automatically generated using a multi-period stage-wise superstructure. Two problems inherent to the heat cascade formulation are encountered during model development. As a first issue, heat networks can form self-sustaining energy loops if their hot and cold streams are not completely embedded within the envelope. This phenomenon is referred to in this work as phantom heat. As a second issue, the heat cascade formulation does not prevent that a thermal storage releases its heat to a cooling technology. To demonstrate the specific features of Syn-E-Sys and its holistic approach towards the synthesis of low carbon energy systems, the model is applied to a generic case study and to a case study from literature. The generic case study is set up to demonstrate the design of an energy system including non-dispatchable renewable energy and energy storage, subject to a carbon emission cap. For this purpose, the year is subdivided into a set of empirically defined time slices that are connected to a hierarchical time structure composed of seasons, daytypes and intra-daily time segments. The results obtained by Syn-E-Sys show a complex interaction between energy supply, energy storage and energy import/export to fulfil energy demands, while keeping carbon emissions below the predefined cap. The model enables optimisation of the intra-annual charge pattern and the capacity of thermal and electrical storage. Moreover, an optimal heat exchanger network is automatically generated. In the second case study, heat recovery is optimised for a drying process in the paper industry. To avoid the energy penalty due to heat exchange restrictions between two separated process parts, heat transfer units need to be optimally integrated. Firstly, a simplified version of the original problem is set up in Syn-E-Sys and the obtained results correspond well to literature. Subsequently, the original problem is extended to demonstrate the optimal integration of heat transfer units in a multi-period situation. In conclusion, Syn-E-Sys facilitates optimal design of low carbon energy systems on business park scale, taking into account the complex time-varying interactions between thermal and electrical energy demand, supply and storage, while the potential for heat recovery is fully exploited